DUSt3R et al. are impressive, but how do they actually work? We investigate this in our project 𝘜𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘔𝘶𝘭𝘵𝘪-𝘝𝘪𝘦𝘸 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴!⁣ We share findings on the iterative nature of reconstruction, the roles of cross and self-attention, and the emergence of correspondences across the network [1/8] ⬇️

Vision and Graphics Trends@si-cv-graphics.bsky.social · 9mo ago

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗩𝗶𝗲𝘄 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 Michal Stary, Julien Gaubil, Ayush Tewari, Vincent Sitzmann arxiv.org/abs/2510.24907 Trending on www.scholar-inbox.com

ICCV 2025 🌺 Aloha from Hawaii! MPI-INF (D2) is presenting 4 papers this year (one Highlight). Thread 👇

I am on my way to #ICCV2025 to present DIY-SC, where we refine foundational features for better semantic correspondence performance. Please come by our poster poster #538 (Session 2) if you're interested or want to chat about my latest project, AnyUp!

Thomas Wimmer@wimmerthomas.bsky.social · 10mo ago

Super excited to introduce ✨ AnyUp: Universal Feature Upsampling 🔎 Upsample any feature - really any feature - with the same upsampler, no need for cumbersome retraining. SOTA feature upsampling results while being feature-agnostic at inference time. 🌐 wimmerth.github.io/anyup/

🤔 What if you could generate an entire image using just one continuous token? 💡 It works if we leverage a self-supervised representation! Meet RepTok🦎: A generative model that encodes an image into a single continuous latent while keeping realism and semantics. 🧵 👇

Super excited to introduce ✨ AnyUp: Universal Feature Upsampling 🔎 Upsample any feature - really any feature - with the same upsampler, no need for cumbersome retraining. SOTA feature upsampling results while being feature-agnostic at inference time. 🌐 wimmerth.github.io/anyup/

Suppose you have separate datasets X, Y, Z, without known correspondences. We do the simplest thing: just train a model (e.g., a next-token predictor) on all elements of the concatenated dataset [X,Y,Z]. You end up with a better model of dataset X than if you had trained on X alone! 6/9

Happy to find my name on the list of outstanding reviewers :] Come and check out our poster on learning better features for semantic correspondence in Hawaii! 📍 Poster #538 (Session 2) 🗓️ Oct 21 | 3:15 – 5:00 p.m. HST genintel.github.io/DIY-SC

#ICCV2025@iccv.bsky.social · 10mo ago

There’s no conference without the efforts of our reviewers. Special shoutout to our #ICCV2025 outstanding reviewers 🫡 iccv.thecvf.com/Conferences/...

🚀 Just accepted to ICCV 2025! In DIY-SC, we improve foundational features using a light-weight adapter trained with carefully filtered and refined pseudo-labels. 🔧 Drop-in alternative to plain DINOv2 features! 📦 Code + pre-trained weights available now. 🔥 Try it in your next vision project!

Olaf Dünkel@oduenkel.bsky.social · last yr.

Are you using DINOv2 for tasks that require semantic features? DIY-SC might be the alternative! It refines DINOv2 or SD+DINOv2 features and achieves a new SOTA on the semantic correspondence dataset SPair-71k when not relying on annotated keypoints! [1/6] genintel.github.io/DIY-SC

The CVML group at the @mpi-inf.mpg.de has been busy for CVPR. Check out our papers and come by the presentations!

Computer Vision and Machine Learning at MPI Informatics@cvml.mpi-inf.mpg.de · last yr.

🎉 Exciting News #CVPR2025! We’re proud to announce that we have 5 papers accepted to the main conference and 7 papers accepted at various CVPR workshops this year! We’re looking forward to sharing our research with the community in Nashville! Stay tuned for more details! ‪‪@mpi-inf.mpg.de‬

Hello world, we are now on Bluesky 🦋! Follow us to receive updates on exciting research and projects from our group! #computervision #machinelearning #research

Had the honor to present "Gaussians-to-Life" at #3DV2025 yesterday. In this work, we used video diffusion models to animate arbitrary 3D Gaussian Splatting scenes. This work was a great collaboration with @moechsle.bsky.social, @miniemeyer.bsky.social, and Federico Tombari. 🧵⬇️

Can you do reasoning with diffusion models? The answer is yes! Take a look at Spatial Reasoning Models. Hats off for this amazing work!

Jan Eric Lenssen@janericlenssen.bsky.social · last yr.

Can image generators solve visual Sudoku? Naively, no, with sequentialization and the correct order, they can! Check out @chriswewer.bsky.social's and Bart's SRM's for details. Project: geometric-rl.mpi-inf.mpg.de/srm/ Paper: arxiv.org/abs/2502.21075 Code: github.com/Chrixtar/SRM

🏔️⛷️ Looking back on a fantastic week full of talks, research discussions, and skiing in the Austrian mountains!

Give a warm welcome to @janericlenssen.bsky.social!

Jan Eric Lenssen@janericlenssen.bsky.social · 2y ago

Hello bluesky-world :) Introducing 𝗠𝗘𝘁𝟯𝗥: 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗩𝗶𝗲𝘄 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗶𝗻 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗜𝗺𝗮𝗴𝗲𝘀. Lacking 3D consistency in generated images is a limitation of many current multi-view/video/world generative models. To quantitatively measure these inconsistencies, check out Mohammad Asims new work!

Quantitative evaluation of diffusion model outputs is hard! We realized that we are often lacking metrics for comparing the quality of video and multi-view diffusion models. Especially the quantification of multi-view 3D consistency across frames is difficult. But not anymore: Introducing MET3R 🧵

MEt3R: Measuring Multi-View Consistency in Generated Images Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, Jan Eric Lenssen tl;dr: DUSt3R + DINO + FeatUp together want to be FID for multiview generation arxiv.org/abs/2501.06336

Zhenjun Zhao@ericzzj.bsky.social · 2y ago

MEt3R: Measuring Multi-View Consistency in Generated Images Mohammad Asim, Christopher Wewer, @wimmerthomas.bsky.social, Bernt Schiele, Jan Eric Lenssen tl;dr: DUSt3R-based method to measure multi-view consistency of generated views without given camera poses arxiv.org/abs/2501.06336

After my general computer vision starter pack is now full (150/150 entries reached), here is one specific to 3D Vision: go.bsky.app/Cfm9XFe